i Sources and Utilization of Health Expenditure

Health expenditure in Chile is financed almost entirely from three sources:

a) OOP spending via co-payments from households made directly

at the point of service,

b) Prepayment plans, comprising premiums paid to FONASA,

ISAPREs and other voluntary insurance schemes; and

c) Government revenues from general taxes.

Analysis of the 2005 ENSGS indicates that the main source of financing is OOP payments (47% of total health expenditure), the least favorable source for households in terms of financial risk protection and equity. The second largest source of financing is premium payments to prepayment schemes (31%), which allow households to lower the risk of incurring catastrophic health expenses. The third largest source is government revenues (21%), which allow the govern- ment to redistribute costs more equitably (Table 1).

EXP

As Table 2 illustrates, OOP expenses are broken down into supplies and drugs (41%), medical consultations (19%), medical treatments and hospitalizations (15%), dental treatments (13%), tests (7%) and others (6%). Unsurprisingly, out-of-pocket spending is used mainly to pay for supplies and drugs, since coverage for medications through FONASA or ISAPREs is insufficient.

Hospitalization and treatment are also relevant in terms of financial protection, as these types of care tend to be the most costly. Households with hospitalization and treatment expenditures in the previous year, which repre- sented 13% of the population, spent $ 1,020 USD PPP per capita annually –almost three times the average. Moreover, these households also spent more on medical consultations, supplies and drugs than the general population. Households with catastrophic health expenses, which represented 6.4% of the total population, spent $ 1,620 USD PPP per capita, 36% of which was used to pay for supplies and drugs, and 30% of which was spent on medical treatment and hospitalization.

Table 1

Health Financing Indicators adjusted by Out-of-Pocket Estimates from ENSGS, 2005 (USD PPP) Government revenue (millions of USD$) 2.862 Prepayment schemes (millions of USD$) 4.231 Out-of-pocket payments at point of service (millions of USD$) 6.389 Non-profit health insurance institutions (millions of USD$) 3 Total health expenditure (millions of USD$) 13.484 Total health expenditure as % of GDP 6.9 Total health expenditure per capita (in USD$) 827 Total out-of-pocket health spending per capita (in USD$) 392

Table 2

Monthly per Capita Spending on Health Services,

by per Capita Expenditure Quintile and Insurance Type, 2005 (USD PPP) Insurance Type

Per Capita Expenditure

Quintile

No

Insurance FONASA A FONASA B/C/D ISAPRE InsuranceOther Total Expenditure on Medical Consultation

Quintile 1 0.6 0.4 0.9 0.9 * 4.0 * 0.7 Quintile 2 2.5 1.7 1.8 3.2 * 12.2 * 2.1 Quintile 3 3.4 2.2 4.1 8.7 6.3 * 4.2 Quintile 4 6.5 4.5 7.6 8.4 9.9 7.5 Quintile 5 13.0 8.2 20.3 23.8 23.3 21.0 Total 4.0 1.4 5.6 16.2 11.4 6.1

Expenditure on Supplies and Drugs

Quintile 1 2.2 2.0 2.8 1.8 * 7.1 * 2.5 Quintile 2 5.5 6.5 6.1 7.9 * 8.6 * 6.2 Quintile 3 7.5 10.4 10.7 10.2 15.3 * 10.4 Quintile 4 13.0 16.8 20.1 18.3 21.4 18.7 Quintile 5 28.5 33.6 41.5 37.1 65.2 39.2 Total 8.9 6.0 13.6 26.3 25.5 13.5

Expenditure on Laboratory Tests and Imaging

Quintile 1 0.1 0.2 0.4 0.5 * 0.0 * 0.3 Quintile 2 1.1 0.4 0.9 0.8 * 0.3 * 0.8 Quintile 3 2.1 1.0 1.4 3.9 3.1 * 1.7 Quintile 4 1.8 1.7 2.6 2.6 3.0 2.4 Quintile 5 1.8 0.5 2.4 4.9 5.5 2.3 Total 1.4 0.8 1.5 2.6 2.4 1.5

Expenditure on Dental Care

Quintile 1 0.2 0.1 0.8 1.0 * 0.1* 0.4 Quintile 2 0.4 0.7 0.7 0.2 * 3.1* 0.7 Quintile 3 1.1 1.1 2.9 3.3 0.2* 2.4 Quintile 4 3.2 1.5 5.2 6.1 3.1 4.8 Quintile 5 8.9 6.2 12.7 22.3 7.0 16.1 Total 2.0 0.6 3.6 13.8 2.9 4.1

Table 2 (continued)

Monthly per Capita Spending on Health Services,

by per Capita Expenditure Quintile and Insurance Type, 2005 (USD PPP) Insurance Type

Per Capita Expenditure

Quintile

No

Insurance FONASA A FONASA B/C/D ISAPRE InsuranceOther Total Expenditure on Hospitalization and Treatment

Quintile 1 0.1 0.1 0.5 0.0* 0.0 * 0.2 Quintile 2 0.8 0.2 0.4 1.0 * 0.7 * 0.4 Quintile 3 0.8 0.3 2.0 6.1 7.5 * 2.1 Quintile 4 3.0 0.6 4.2 5.2 2.1 3.9 Quintile 5 18.2 16.5 16.5 29.9 14.0 22.4 Total 2.9 0.6 3.5 18.0 5.0 4.8

Expenditure on Other Health Services

Quintile 1 0.2 0.1 0.3 0.4 * 0.3 * 0.2 Quintile 2 1.0 0.3 0.6 0.5 * 1.7* 0.6 Quintile 3 1.0 1.0 1.4 2.6 1.5 * 1.4 Quintile 4 1.6 2.0 2.5 2.4 2.1 2.3 Quintile 5 4.7 3.4 5.7 8.3 2.7 6.6 Total 1.3 0.5 1.7 5.4 1.8 1.9

Total Health Expenditure

Quintile 1 3.4 3.0 5.7 4.7 * 11.5 * 4.3 Quintile 2 11.4 9.8 10.6 13.5 * 26.5 * 10.9 Quintile 3 15.9 16.1 22.6 34.8 33.9 * 22.2 Quintile 4 29.2 27.3 42.2 42.9 41.7 39.6 Quintile 5 80.3 69.6 106.7 128.3 131.5 113.9 Total 20.8 9.6 30.3 84.7 52.1 32.7

Note: * Statistics calculated with less than 30 observations. Source: Authors’ calculations based on ENSGS, 2005.

III.ii. Insurance

In order to estimate the effects of insurance by comparing the insured with the uninsured, it is critical to first acknowledge the differences between the two populations. As explained above, the population employed in the formal sector is required to obtain insurance, whereas the population without formal employ- ment may choose whether to obtain insurance or not. Therefore, an obvious difference between the two populations is that the insured group includes people with formal or informal employment as well as unemployed persons, whereas the uninsured group includes only people employed in the informal sector or unemployed persons. The choice whether to obtain insurance through FONASA or an ISAPRE is strongly determined by socioeconomic level. How- ever, other factors also determine whether an individual decides to become insured, and the choice of insurer. Table 3 shows the results of three regression analyses with a probit model that predicts the likelihood of obtaining insurance through FONASA Group A, FONASA Groups B, C or D, or an ISAPRE, depending on the following household characteristics: geographical region, quintile of total household expenditure per capita, life cycle, household size and share of household members with formal employment.

The data show that households with greater health needs, specifically households with older adults, tend to obtain insurance through FONASA. Because of their greater health risks, older adults would be expected to show a stronger preference for obtaining insurance than the rest of the population. They would also be expected to prefer FONASA, given that ISAPRE premi- ums increase proportionately with health risk whereas the FONASA premium remains unchanged. In summary, the FONASA, ISAPRE, and uninsured pop- ulations differ in socioeconomic level and health needs, and may have different levels of health utilization and expenditure regardless of the effect of insurance. This makes it necessary to control for these variables before the populations can be compared.

Table 3

Probit Regressions of the Probability of Insuring with FONASA A, FONASA B/C/D or an ISAPRE

Model: FONASA A FONASA B/C/D ISAPRE

Observations: 1,354.000 2,899.000 1,137.000

F: 10.330 9.780 22.710

Prob > F: 0.000 0.000 0.000

R-2 0.094 0.034 0.271

Variables included in the model dy/dx dy/dx dy/dx

Geographical Region

Region II Omitted

Region V 0.237* -0.061 -0.168***

Region VIII 0.065 -0.109** -0.244**

Metropolitan region 0.144 -0.075** -0.057

Quintile of Total Household Expenditure per Capita

Quintile 1 Omitted

Quintile 2 -0.096** 0.064*** 0.334***

Quintile 3 -0.170*** 0.074*** 0.463***

Quintile 4 -0.439*** 0.028 0.579***

Quintile 5 -0.493*** 0.053* 0.808***

Household Life Cycle

w/o older adult and w/o child Omitted

w/ child less than 5 yrs -0.001 -0.035 -0.016

w/ adult over 65 yrs and w/o child 0.041 0.104*** -0.018 w/ adult >65 and child <5 0.235*** 0.116*** 0.043

Household Size

1-2 members Omitted

3-4 members -0.040 -0.012 0.083

5+ members -0.124** -0.026 0.259***

% of members w/ formal employment -0.045 0.209*** 0.569***

Note: *Level of significance 0.1; **Level of significance 0.05; ***Level of significance 0.01. Source: Authors’ calculations based on ENSGS, 2005.